Topic Node Generation for Matching Users with Varying Interest Granularity
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Solution Overview
Problem
Conventional communication assistance techniques fail to effectively match users based on varying levels and granularity of interest, persistence to topics, and relevance of topics to individual users, making it difficult to find common ground for smooth communication, especially when interacting with new people.
Innovation Solution
A communication assistance device and method that extracts meta information from user content, calculates topic appropriateness values, generates and expands topic nodes, and extracts common topic candidates using semantic relationships and inter-item distances, allowing for the display and sharing of relevant content among users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If matching is performed only using topics included in user profiles, then the system is simple to implement, but it fails to provide a mechanism for finding common ground between groups of users with varying levels and granularity of interest
Solution Approach 1:
The patent segments topics into different levels of granularity by creating topic nodes at multiple hierarchical levels. Each topic node represents a specific level of detail, allowing the system to match users based on their varying interest levels. This segmentation enables flexible matching without requiring a completely complex reimplementation of the entire matching mechanism.
Solution Approach 2:
The patent introduces a new dimension to topic matching by creating a hierarchical structure with multiple levels of topic nodes. This dimensional approach allows the system to accommodate users with different granularities of interest by navigating through the hierarchical levels, thereby improving adaptability while managing complexity through structured organization.
2Measurement precision
If the system considers varying degrees of users' persistence to a topic, then topic matching becomes more accurate, but the evaluation process becomes more complex
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing persistence values for topic nodes before the actual matching process. These persistence values are prepared in advance and can be directly retrieved during user matching, thereby improving measurement precision without adding significant complexity to the real-time evaluation process.
Solution Approach 2:
The system uses self-service by automatically calculating and storing persistence values as part of the normal operation. The persistence evaluation is integrated into the existing data collection and processing mechanisms, allowing the system to improve accuracy through persistent tracking without requiring separate complex evaluation processes.
3Loss of information
If the system provides sufficient information about relevance of a topic to each user's own interest, then communication can proceed more effectively, but the amount of information to be processed increases
Solution Approach 1:
The patent applies local quality by providing topic relevance information specifically tailored to each user's interest level and granularity preference. Rather than uniformly providing the same level of detail to all users, the system customizes the information quality according to each user's characteristics, thereby reducing the overall information processing burden while maintaining completeness where needed.
Solution Approach 2:
The system dynamically adjusts the amount and level of topic relevance information provided based on real-time user interactions and preferences. This dynamic adaptation allows the system to provide sufficient information for effective communication while minimizing unnecessary information processing by adjusting the information quantity according to user needs and context.
Data Source
AI summary
A communication assistance device (100) enables smooth and active communication between users, and includes a topic node generation unit (103) that calculates a topic appropriateness value based on the number of pieces of content from which meta information has been extracted or the number of times each piece of content including the meta information has been viewed, and generates a first topic node including the meta information and the topic appropriateness value, an integrated topic node generation unit (104) that obtains first topic nodes for a plurality of users, and generates, for every item of meta information, an integrated topic node storing the topic appropriateness value corresponding to each user, a topic node expansion unit (105) that generates an expanded topic node as a new topic node based on the item of meta information for the integrated topic node, a common-topic-candidate extraction unit (107) that extracts a common topic candidate between the users from the integrated topic node or the expanded topic node, a display unit (108) that displays the common topic candidate, and an input unit (110) that receives input of at least one of the displayed common topic candidates.